Performance Analysis of Multi Spectral Band Image Compression using Discrete Wavelet Transform
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Copyright: © 2020 R. Kousalyadevi and S. S. Ramakrishnan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Efficient and effective utilization of transmission bandwidth and storage capacity have been a core area of research for remote sensing images. Hence image compression is required for multi-band satellite imagery. In addition, image quality is also an important factor after compression and reconstruction. Approach: In this investigation, the discrete wavelet transform is used to compress the Landsat5 agriculture and forestry image using various wavelets and the spectral signature graph is drawn. Results: The compressed image performance is analyzed using Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR). The compressed image using dmey wavelet is selected based on its Digital Number Minimum (DNmin) and Digital Number Maximum (DNmax). Then it is classified using maximum likelihood classification and the accuracy is determined using error matrix, kappa statistics and over all accuracy. Conclusion: Hence the proposed compression technique is well suited to compress the agriculture and forestry multi-band image.
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- Compression Ratio (CR)
- Digital Number minimum (DNmin)
- Peak Signal to Noise Ratio (PSNR)
- Remote Sensing (RS)
- Mean Square Error (MSE)